Department of Anesthesiology, Second Affiliated Hospital of Zunyi Medical University, Guizhou Province, China.
Department of Anesthesiology, Second Affiliated Hospital of Zunyi Medical University, Guizhou Province, China.
Int J Med Inform. 2024 Dec;192:105609. doi: 10.1016/j.ijmedinf.2024.105609. Epub 2024 Aug 30.
Deep vein thromboembolism (DVT) is a common postoperative complication with high morbidity and mortality rates. However, the safety and effectiveness of using prophylactic anticoagulants for preventing DVT after spinal surgery remain controversial. Hence, it is crucial to predict whether DVT occurs in advance following spinal surgery. The present study aimed to establish a machine learning (ML)-based prediction model of DVT formation following spinal surgery.
We reviewed the medical records of patients who underwent elective spinal surgery at the Third Affiliated Hospital of Zunyi Medical University (TAHZMU) from January 2020 to December 2022. We ultimately selected the clinical data of 500 patients who met the criteria for elective spinal surgery. The Boruta-SHAP algorithm was used for feature selection, and the SMOTE algorithm was used for data balance. The related risk factors for DVT after spinal surgery were screened and analyzed. Five ML algorithm models were established. The data of 150 patients treated at the Affiliated Hospital of Zunyi Medical University (AHZMU) from July 2023 to October 2023 were used for external verification of the model. The area under the curve (AUC), geometric mean (G-mean), sensitivity, accuracy, specificity, and F1 score were used to evaluate the performance of the models.
The results revealed that activated partial thromboplastin time (APTT), age, body mass index (BMI), preoperative serum creatinine (Crea), anesthesia time, rocuronium dose, and propofol dose were the seven important characteristic variables for predicting DVT after spinal surgery. Among the five ML models established in this study, the random forest classifier (RF) showed superior performance to the other models in the internal validation set.
Seven preoperative and intraoperative variables were included in our study to develop an ML-based predictive model for DVT formation following spinal surgery, and this model can be used to assist in clinical evaluation and decision-making.
深静脉血栓形成（DVT）是一种常见的术后并发症，具有较高的发病率和死亡率。然而，在脊柱手术后使用预防性抗凝剂预防 DVT 的安全性和有效性仍存在争议。因此，预测脊柱手术后是否发生 DVT 至关重要。本研究旨在建立一种基于机器学习（ML）的脊柱手术后 DVT 形成预测模型。
我们回顾了遵义医科大学第三附属医院（TAHZMU） 2020 年 1 月至 2022 年 12 月期间接受择期脊柱手术的患者的病历。最终，我们选择了符合择期脊柱手术标准的 500 例患者的临床数据。使用 Boruta-SHAP 算法进行特征选择，使用 SMOTE 算法进行数据平衡。筛选和分析脊柱手术后 DVT 的相关危险因素。建立了 5 种 ML 算法模型。使用遵义医科大学附属医院（AHZMU） 2023 年 7 月至 2023 年 10 月收治的 150 例患者的数据对模型进行外部验证。采用曲线下面积（AUC）、几何平均值（G-mean）、敏感性、准确性、特异性和 F1 评分评估模型性能。
结果表明，活化部分凝血活酶时间（APTT）、年龄、体重指数（BMI）、术前血清肌酐（Crea）、麻醉时间、罗库溴铵剂量和丙泊酚剂量是预测脊柱手术后 DVT 的七个重要特征变量。在本研究中建立的 5 种 ML 模型中，随机森林分类器（RF）在内部验证集中的性能优于其他模型。
本研究纳入了 7 个术前和术中变量，建立了一种基于 ML 的脊柱手术后 DVT 形成预测模型，该模型可用于辅助临床评估和决策。
